# Reducing Warning Errors in Driver Support with Personalized Risk Maps

arXiv: https://arxiv.org/abs/2410.02148 · HTML: https://mlsanity.com/paper/2410.02148/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 13 references that are in the map.
- First posted on arXiv 3 Oct 2024.

## Builds on (most cited first, 3 of 3)

- [A Learning-Based Approach for Lane Departure Warning Systems with a Personalized Driver Model](https://mlsanity.com/paper/1702.01228/) (2017)
- [Online and Predictive Warning System for Forced Lane Changes using Risk Maps](https://mlsanity.com/paper/2303.08458/) (2023)
- [Considering Human Factors in Risk Maps for Robust and Foresighted Driver Warning](https://mlsanity.com/paper/2306.03849/) (2023)

## Abstract (arXiv)

We consider the problem of human-focused driver support. State-of-the-art personalization concepts allow to estimate parameters for vehicle control systems or driver models. However, there are currently few approaches proposed that use personalized models and evaluate the effectiveness in the form of general risk warning. In this paper, we therefore propose a warning system that estimates a personalized risk factor for the given driver based on the driver's behavior. The system afterwards is able to adapt the warning signal with personalized Risk Maps. In experiments, we show examples for longitudinal following and intersection scenarios in which the novel warning system can effectively reduce false negative errors and false positive errors compared to a baseline approach which does not use personalized driver considerations. This underlines the potential of personalization for reducing warning errors in risk warning and driver support.